File size: 3,953 Bytes
d0f55c6 15c8167 26e855f 15c8167 460930f 15c8167 30a0c61 d0f55c6 15c8167 460930f 30a0c61 15c8167 8e404a5 15c8167 611a3ed 15c8167 8e404a5 15c8167 611a3ed 15c8167 d0f55c6 8e404a5 c2c9efa 8e404a5 d0f55c6 8e404a5 d0f55c6 da4a3b1 15c8167 d0f55c6 611a3ed d0f55c6 15c8167 bd64e7a 15c8167 611a3ed 15c8167 26e855f 15c8167 9c39267 30a0c61 15c8167 bf6ab81 9c39267 15c8167 9c39267 15c8167 611a3ed 9c39267 30a0c61 9c39267 611a3ed 15c8167 26e855f a4b20f4 33d0dfb 26e855f 8f7c83f 611a3ed |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 |
import asyncio
import gradio as gr
import numpy as np
import pandas as pd
from huggingface_hub import HfFileSystem
import src.constants as constants
from src.hub import load_file
def fetch_result_paths():
fs = HfFileSystem()
paths = fs.glob(f"{constants.RESULTS_DATASET_ID}/**/**/*.json")
return paths
def sort_result_paths_per_model(paths):
from collections import defaultdict
d = defaultdict(list)
for path in paths:
model_id, _ = path[len(constants.RESULTS_DATASET_ID) + 1 :].rsplit("/", 1)
d[model_id].append(path)
return {model_id: sorted(paths) for model_id, paths in d.items()}
def update_load_results_component():
return (gr.Button("Load", interactive=True),) * 2
async def load_results_dataframe(model_id, result_paths_per_model=None):
if not model_id or not result_paths_per_model:
return
result_paths = result_paths_per_model[model_id]
results = await asyncio.gather(*[load_file(path) for path in result_paths])
data = {"results": {}, "configs": {}}
for result in results:
data["results"].update(result["results"])
data["configs"].update(result["configs"])
model_name = result.get("model_name", "Model")
df = pd.json_normalize([data])
# df.columns = df.columns.str.split(".") # .split return a list instead of a tuple
return df.set_index(pd.Index([model_name])).reset_index()
async def load_results_dataframes(*model_ids, result_paths_per_model=None):
result = await asyncio.gather(
*[load_results_dataframe(model_id, result_paths_per_model) for model_id in model_ids]
)
return result
def display_results(task, *dfs):
dfs = [df.set_index("index") for df in dfs if "index" in df.columns]
if not dfs:
return None, None
df = pd.concat(dfs)
df = df.T.rename_axis(columns=None)
return display_tab("results", df, task), display_tab("configs", df, task)
def display_tab(tab, df, task):
df = df.style.format(escape="html", na_rep="")
df.hide(
[
row
for row in df.index
if (
not row.startswith(f"{tab}.")
or row.startswith(f"{tab}.leaderboard.")
or row.endswith(".alias")
or (
not row.startswith(f"{tab}.{task}")
if task != "All"
else row.startswith(f"{tab}.leaderboard_arc_challenge")
)
)
],
axis="index",
)
df.apply(highlight_min_max, axis=1)
start = len(f"{tab}.leaderboard_") if task == "All" else len(f"{tab}.{task} ")
df.format_index(lambda idx: idx[start:].removesuffix(",none"), axis="index")
return df.to_html()
def update_tasks_component():
return (
gr.Radio(
["All"] + list(constants.TASKS.values()),
label="Tasks",
info="Evaluation tasks to be displayed",
value="All",
visible=True,
),
) * 2
def clear_results():
# model_id_1, model_id_2, dataframe_1, dataframe_2, load_results_btn, load_configs_btn, results_task, configs_task
return (
None,
None,
None,
None,
*(gr.Button("Load", interactive=False),) * 2,
*(
gr.Radio(
["All"] + list(constants.TASKS.values()),
label="Tasks",
info="Evaluation tasks to be displayed",
value="All",
visible=False,
),
)
* 2,
)
def highlight_min_max(s):
if s.name.endswith("acc,none") or s.name.endswith("acc_norm,none") or s.name.endswith("exact_match,none"):
return np.where(s == np.nanmax(s.values), "background-color:green", "background-color:#D81B60")
else:
return [""] * len(s)
def display_loading_message_for_results():
return ("<h3 style='text-align: center;'>Loading...</h3>",) * 2
|